The majority of emerging infectious diseases that affect humans are zoonotic; diseases that are transmittable between animals and humans. The health of animals can be a sentinel for zoonotic diseases in humans. Unfortunately, most local and state health department epidemiologists do not have automated access to this data. Using data on animal health to predict risk of zoonotic diseases in humans could allow epidemiologists to detect public health threats sooner. Earlier detection means earlier intervention which could lead to less morbidity and mortality. This career award will study this problem by: gaining an understanding of the data and technology needs for zoonotic disease surveillance at the local and state administrative level (Aim 1), applying these needs to the development of a pilot 'animal-human' surveillance system that integrates health data of animals and humans (Aim 2), and evaluating the potential of this novel system for zoonotic disease surveillance (Aim 3). The completion of this 3-step process will establish a framework for integrating health data of animals and humans. Aim 1 will be addressed through a mixed model design using qualitative observation of applied zoonotic surveillance, and an electronic survey to asses the data and technology needs involved in this process. The qualitative portion will consist of observation and interviews of individuals who practice zoonotic surveillance at health departments and diagnostic laboratories in Connecticut. Aim 2 will entail the development of a pilot animal-human zoonotic surveillance system, based on the identified needs from Aim 1, that contains a usability-tested interface for the analysis of disease trends in humans. The final Aim (3) will serve to asses the potential of an animal-human zoonotic surveillance system by conducting a between subjects comparative evaluation of the 'animal-human' system vs. a 'human-only' (a system containing only human public health data). The two systems will be evaluated by current and future professionals (graduate students) in Connecticut for analyzing trends of different zoonotic diseases in humans. This work will provide a framework for integrating animal and human data and demonstrate the potential of this synergy in surveillance of zoonotic disease. It will hopefully lead to the development of powerful surveillance systems in local and state health departments.